Inspired by the great self-supervised learning performance of MAE  [1]} in 2D images, we design the pretraining network of masked autoencoders for LiDAR-based 3D object detectors in order to learn representative features. With the \(n_s\)  unlabeled point cloud data \(\left\lbrace \textbf {X}^i \right\rbrace _{i=1}^{n_s}\) , we aim to first pre-train the masked autoencoders network \(\phi _{pre}\)  with only the point clouds data \(\left\lbrace \textbf {X}^i\right\rbrace _{i=1}^{n_s}\)  to learn the high-level semantics. Then we use the pre-trained model \(\phi _{pre}\)  to warm up the 3D object detection network \(\phi _{s}\)  to improve the detection performance. We also use the pre-trained network model \(\phi _{pre}\)  to domain adaptative task for the target point clouds \(\left\lbrace \textbf {X}^j\right\rbrace _{j=1}^{n_t}\) . The proposed method is shown in Figure REF .
